gabbe-kit

v1.1.1

Published

5 hours ago

GABBE - Generative Architectural Brain Base Engine. A Markdown agentic engineering kit installer (Python-independent). Wires AGENTS.md, skills, and per-agent rule files into any project.

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andreibesleaga

agentic ai engineering cli llm agents skills claude cursor copilot

GABBE (Generative Architectural Brain Base Engine)

Agentic Software R&D Engineering Kit

Quick Reference

GABBE Agentic Software R&D Engineering Kit

What is this?

Agentic code development and governance via capability layers and software development cycle gates verifications and alignment (including best practices for security and human-in-the-loop, complete framework).
The first published open-source project world-wide that features a complete agentic coding kit/framework with cognitive brain loop, skills, SDLC workflow, etc.
Universal kit for Software and AI coding agents: Claude Code, Cursor, Windsurf, Cline, Aider, Devin, Gemini, Antigravity, OpenCode, Zed, Continue, Roo Code, Kilo Code, OpenAI/Codex, GitHub Copilot, VS Code.
Drop-in context kit that turns any AI coding agent into a governed engineering team for developing software.
Based on Software Engineering & Architecture Practices and Procedures.
Works for any project type, new or existing, any language, any team size.
Write Once, Run Everywhere: SKILLS for Cursor (.mdc), VS Code / Copilot (folder/SKILL.md), Claude (.claude/skills), Gemini, and the universal .agents/skills/ tree read by Antigravity, OpenCode, and any agentskills.io tool.
Trivially installable: npx gabbe-kit init (Python-independent) or curl -fsSL …/install.sh | sh, plus the Python wizard / PyPI. Move work between agents anytime (portable state export/import).
First-class Observability (decision/cost traces, OTel GenAI conventions), Spec-Driven development (spec → evals → test → code), and a manager-not-operator human↔agent collaboration model.
The system features an experimental Meta-Cognitive Orchestrator "Brain" (Neurocognitive based architecture derived from Neuroscience, Cognitive Psychology, Epistemology, treating the Software System not as a machine, but as a Cognitive Entity), using Active Inference to plan, route, and optimize work.
The system features a Multi-Agent Swarm "Loki" Engineering Team (30+ specialized agent roles for large projects), providing episodic and semantic memory, project history auditing and checkpoints.
Experimental support for budget enforcement, tokens, hard stops, policy rules, cli tool gateway (via MCP server), audit tracing and logs, human escalation, and deterministic replay, with built-in rules for agents to select the best specialized skills/guides, proactively recommend necessary MCP servers, and default to continuous cost & budget optimization—always requiring human approval for expensive operations.
v1.0 extends the framework to a cradle-to-grave ADLC (S00–S13) — Day-0 Strategy & Discovery (S00) through the S01–S10 SDLC to Day-2 Operate/Evolve/Decommission (S11–S13), grounded in named industry methods (ADD 3.0, ATAM, Wardley, JTBD, RICE, DORA/SPACE, ADKAR) — and adds an evals + standards-grounded guardrails layer (eval-driven development, LLM-as-judge, RAG/trajectory evals, plus prompt-injection-defense and output-validation mapped to OWASP LLM Top 10 / NIST AI RMF / MITRE ATLAS / ISO 42001 / EU AI Act), advanced testing (property-based, metamorphic, chaos/fault-injection, and mutation testing; gabbe verify --chaos), and one-command, multi-OS install with autodetect (gabbe doctor reports OS/arch, runtimes, and detected agents; update/uninstall are manifest-backed for gabbe-CLI-managed kits via .gabbe/manifest.json). The "world-first" framing stays honest: the self-evolving "genes" / brain-inference-via-skills model is a conceptual framing — the production brain is epsilon-greedy with a monotonic success-rate, and evals/PBT sample and raise confidence, they do not prove.

It contains:

214 Skills (specialized capabilities)
100 Templates (standardized documents)
86 Guides (language & domain expertise)
36 Personas (specialized roles)
50+ MCP servers (configuration and guides for AI tools)
Brain Mode (meta-cognitive orchestration)
Loki Mode (multi-agent swarm engineering personas team for large projects)

214 Skills · 100 Templates · 86 Guides · 36 Personas · 50+ MCPs · Loki / Brain Mode CLI

Full documentation: README_FULL.md · Full quick guide: QUICK_GUIDE.md · Quick commands: QUICK_COMMANDS.md · CLI reference: CLI_REFERENCE.md · MCP servers: MCP_CONFIGURATIONS.md · Platform Controls: PLATFORM_CONTROLS.md · Verification Guide: VERIFICATION_GUIDE.md

Online articles: Medium, Substack, TechRxiv

Example project where GABBE is used: Agentic AI Medical Imagery Diagnostic Helper

⚡ Automated Setup (Recommended)

All install options at a glance (every channel is a single command — pick one):

| Channel | One-command install | Notes | |---|---|---| | npm / Node | npx gabbe-kit init | Python-independent; bundles the kit & wires detected agents | | PyPI | pipx install gabbe | adds the gabbe CLI (doctor/brain/route/gateway); install the kit via npx / curl / checkout | | Shell bootstrap | curl -fsSL https://raw.githubusercontent.com/andreibesleaga/GABBE/main/install.sh \| sh | picks the best available installer | | Git checkout | git clone https://github.com/andreibesleaga/GABBE && cd GABBE && python3 scripts/init.py | the interactive wizard |

On npm the package is gabbe-kit (npm refuses the unscoped gabbe); on PyPI it is gabbe. The installed command is gabbe either way. After installing, run gabbe doctor for an environment + install report. Full guide: docs/INSTALL.md.

Universal, Python-independent (one command):

npx gabbe-kit init                       # Node installer — bundles the kit, wires every agent
npx gabbe-kit init --agents claude,cursor,antigravity,opencode --yes   # non-interactive
npx gabbe-kit init --force               # re-template even preserved files (each is backed up first)
# or, without npm:
curl -fsSL https://raw.githubusercontent.com/andreibesleaga/GABBE/main/install.sh | sh

Never-clobber. Both installers — the Node installer (npx gabbe-kit init) and the Python wizard (gabbe setup / python scripts/init.py) — back up ANY differing pre-existing file to <name>.gabbe-bak before refreshing it; nothing you already had is silently overwritten. The preserve-set (AGENTS.md, CONSTITUTION.md, TASKS.md, policies.yml, config.json) is left untouched unless you pass --force, which opts into re-templating them too (still backing each up first).

Python / PyPI:

pipx install gabbe            # installs the `gabbe` CLI (doctor / brain / route / gateway)
# the kit is Python-independent — land it into a project with:
npx gabbe-kit init            # (or `curl … | sh`)
python3 scripts/init.py       # …or the wizard from a checkout (equivalent to `gabbe setup`)

The installer is a Universal Skill Compiler — it generates the correct format for each AI tool:

Cursor: .cursor/rules/*.mdc (agent-requested rules, intelligently selected by description)
VS Code / Copilot: .github/skills/<slug>/SKILL.md
Claude Code: .claude/skills/<slug>/SKILL.md
Gemini: .gemini/settings.json + GEMINI.md
Antigravity / OpenCode (and any agentskills.io tool): the universal .agents/skills/<slug>/SKILL.md tree (+ opencode.json for OpenCode)
Zed / Continue / Roo Code / Kilo Code: root AGENTS.md (agents.md standard) + each tool's rules file
Every install also writes a root AGENTS.md (the agents.md open standard).

Greenfield vs. brownfield (autodetect). In an empty directory the wizard runs the greenfield flow unchanged. In an existing codebase (detected via package.json, pyproject.toml, go.mod, Cargo.toml, composer.json, pom.xml, Gemfile, a .git repo, etc.) it adds a Mode question — greenfield build vs Upgrade / Refactor existing — prefills the detected language / framework / package-manager / project-name as defaults, and in refactor mode scaffolds a BROWNFIELD_ONBOARDING.md discovery brief (map → assess → baseline → backlog) instead of greenfield mission docs.

Steps after running the wizard:

Verify Context
- Open agents/AGENTS.md and check the Tech Stack section and other [PLACEHOLDER] or Optional sections.
- The wizard auto-fills derivable [PLACEHOLDER:] fields — dev/test/lint/format/typecheck/coverage commands (by package manager), repo_url (from the git remote), and ci_cd / deployment_target (from detected files). Fields it can't derive are kept and tagged , and the installer prints an end-of-install warning listing them so blank fields are never shipped silently. Fill them in or re-run gabbe setup.
- Open agents/CONSTITUTION.md and review project laws.
Feed the Mission
- The script generates BOOTSTRAP_MISSION.md (or SETUP_MISSION.md if dynamic setup is disabled) in your root.
- Copy its content and paste it into your AI Agent's chat window.
- This aligns the agent with your project context immediately.
Git Tracking
- If you want to keep the initial structure of agents/memory/ and project/ in your repository but prevent Git from tracking the continuous autonomous modifications your agents will make to them locally, run:
```
git ls-files agents/memory/ project/ | xargs git update-index --skip-worktree
```

Manual Setup:

cp -r GABBE/agents .
chmod +x agents/setup-context.sh && agents/setup-context.sh

🌍 Cross-Platform Support

Linux / macOS / WSL: Native support.
Windows (Native):
- Use python scripts/init.py (Symlinks automatically fallback to file copies if needed).
- Use agents/scripts/setup-context.ps1 instead of .sh.

🚀 Common Actions (Copy-Paste Prompts)

Strategy & Ideation (Step 0)

"Use business-case/strategy skills to validate exactly why we are building [description] and who it is for."

New Project from Scratch

"Read AGENTS.md. I want to build [description]. Start with spec-writer skill."

Flow: Strategy → Spec → Design → Tasks → TDD Implementation → Security → Deploy

Resume Existing Project

"Read AGENTS.md and agents/memory/PROJECT_STATE.md. Resume the project."

Fix a Bug

"Read AGENTS.md. Bug: [description]. Use debug skill with TDD."

Flow: Reproduce → Root Cause → Failing Test → Fix → Green → Regression Check

Refactor / Pay Tech Debt

"Use tech-debt skill on [directory]. Then refactor the top-priority item."

Security Audit

"Run security-audit skill on the entire codebase."

Architecture Review

"Run arch-review skill. Check for SOLID violations and coupling."

"Use the performant-nodejs skill to audit the current Node.js architecture for scalability bottlenecks and propose optimizations."

"Use the performant-laravel skill to audit the current Laravel architecture for scalability bottlenecks and propose optimizations."

"Use the performant-python skill to audit the current Python architecture for scalability bottlenecks and propose optimizations."

"Use the performant-go skill to audit the current Go architecture for scalability bottlenecks and propose optimizations."

"Use the performant-ai skill to audit the current AI/LLM architecture for latency and cost bottlenecks."

"Use the time-complexity skill to scan src/ for Big-O complexity hotspots and identify functions worse than O(n)."

Software Engineering & System Architecture

"Act as a Principal Staff Engineer. Review the codebase in [directory] and generate a C4 system architecture diagram (Context and Container levels). Identify any bottlenecks and propose scaling strategies."

"Use the visual-whiteboarding skill. Connect to the Draw.io/Miro MCP and generate a visual spatial architecture diagram for the current microservice layout."

"Use the design-patterns and domain-model skills. We are building a [feature segment]. Propose the optimum architecture pattern (e.g. Event-driven, CQRS, Hexagonal) and define the core domain entities."

Vibe-Coding (Creative Frontend)

"Use the vibe-coding skill. Build a [component/page] using [framework]. I want it to feel [aesthetic, e.g. glassmorphism, cyberpunk, sleek corporate]. Include micro-animations and smooth transitions. Prioritize visual WOW over generic utility."

Activate Brain Mode (Complex Goals)

"Activate Brain Mode. Goal: [build X / migrate Y / solve Z]."

Uses Active Inference to plan, route between local/remote models, and learn from past outcomes.

Activate Loki Mode (Large Projects)

Using Pure Agent Mode (No CLI):

"Activate agents/skills/brain/loki-mode.skill.md. Goal: [build X]. Do not ask me for permission unless you hit a mandatory Human Approval Gate or a task requires True A2A Delegation."

Multi-agent swarm with 30+ specialized personas for projects >5 features or >20 files.

End-to-End Workflow & Architecture

Visual Overview (Mermaid)

graph TD
    %% Phase 1: Setup
    subgraph Setup [1. Setup Phase]
        Start([Start]) --> Init[Run init.py]
        Init --> Mission[Feed BOOTSTRAP_MISSION.md or SETUP_MISSION.md]
        Mission --> Config[Edit AGENTS.md]
    end

    %% Phase 2: Definition
    subgraph Definition [2. Definition Phase]
        Config --> Spec[Trigger: spec-writer.skill]
        Spec --> PRD[Artifact: PRD.md]
        PRD --> Review1{Human Approve?}
        Review1 -- No --> Spec
    end

    %% Phase 3: Design
    subgraph Design [3. Design Phase]
        Review1 -- Yes --> Plan[Trigger: arch-design.skill]
        Plan --> Arch[Artifact: PLAN.md + C4]
        Arch --> Review2{Human Approve?}
        Review2 -- No --> Plan
    end

    %% Phase 4: Execution
    subgraph Execution [4. Execution Loop]
        Review2 -- Yes --> Tasks[Trigger: Decompose project/TASKS.md]
        Tasks --> LoopCheck{Tasks Remaining?}
        LoopCheck -- Yes --> Pick[Pick Task]
        Pick --> TDD[Trigger: tdd-cycle.skill]
        TDD --> Red[Test Fails]
        Red --> Green[Implement Pass]
        Green --> Refactor[Refactor]
        Refactor --> Verify[Audit Log]
        Verify --> LoopCheck
    end

    %% Phase 5: Delivery
    subgraph Delivery [5. Delivery Phase]
        LoopCheck -- No --> Integrity[Trigger: integrity-check.skill]
        Integrity --> Security[Trigger: security-audit.skill]
        Security --> HumanRev{Human Review?}
        HumanRev -- No --> Fix[Fix Issues]
        Fix --> Integrity
        HumanRev -- Yes --> Deploy[Deploy]
        Deploy --> End([Done])
    end

Text Overview (ASCII)

[START]
   |
[INSTALL] python3 scripts/init.py -> Generates BOOTSTRAP_MISSION.md (or SETUP_MISSION.md)
   |
[SETUP]   Feed Mission to Agent -> Edit AGENTS.md (Stack/Rules)
   |
[DEFINE]  "Start new feature" -> spec-writer.skill -> PRD.md
   |      (Human Reviews & Approves PRD)
   v
[DESIGN]  Plan Architecture -> arch-design.skill -> PLAN.md + ADRs
   |      (Human Reviews & Approves Plan)
   v
[TASKS]   Decompose to project/TASKS.md (Atomic 15-min units)
   |
   +---> [IMPLEMENTATION LOOP] ----------------------------------+
   |     1. Pick Task from project/TASKS.md                               |
   |     2. Write Failing Test (Red)                              |
   |     3. Write Code to Pass (Green)                            |
   |     4. Refactor & Clean Up                                   |
   |     5. Verify (Tests + Lint) & Log to AUDIT_LOG.md           |
   |     (Repeat until project/TASKS.md is empty)                         |
   +-------------------------------------------------------------+
   |
[VERIFY]  integrity-check.skill -> security-audit.skill
   |      (Human Final Review)
   v
[DEPLOY]  Merge PR -> Staging -> Production -> [DONE]

System Architecture

How the pieces fit together to create a "Cognitive Entity".

Visual Architecture (Mermaid)

graph TB
    subgraph Human ["User (Steering Wheel)"]
        H1[Strategy & Goals]
        H2[Review & Approval]
    end

    subgraph Agent ["Agent / Brain (Engine)"]
        B1[Active Inference Loop]
        B2[Task Router]
    end

    subgraph Context ["Project Context"]
        C1[AGENTS.md]
        C2[CONSTITUTION.md]
    end

    subgraph Tools ["Capability Layer"]
        S[214 Skills]
        T[100 Templates]
        G[86 Guides]
    end

    subgraph Memory ["Memory System"]
        M1[Working Memory]
        M2["Episodic (Logs)"]
        M3["Semantic (Facts)"]
    end

    H1 --> B1
    C1 --> B1
    B1 --> B2
    B2 --> S
    S --> T
    S --> M2
    M3 --> B1
    S --> H2
    H2 -- Feedback --> B1

Text Architecture (ASCII)

       [HUMAN USER]
            | (Goal/Feedback)
            v
    +-------------------+      +------------------+
    |   AGENT BRAIN     | <--- |  PROJECT CONTEXT |
    | (Active Inference)|      | (AGENTS/Rules)   |
    +--------+----------+      +------------------+
             |
             v
    [ROUTER & ORCHESTRATOR]
             |
    +--------+--------------------------+
    |                                   |
[SKILLS] (Function)               [MEMORY] (Context)
    |                                   |
    +-> [Coding]                  +-> [Episodic Logs]
    +-> [Architecture]            +-> [Semantic Facts]
    +-> [Security]                +-> [Continuity]
    +-> [Ops / SRE]                     |
            |                           |
            v                           v
    [TEMPLATES] (Structured Output) <---+

Spec-Driven SDLC Lifecycle

The "Golden Path" for every feature.

Visual SDLC (Mermaid)

flowchart TD
    S0[S00: Strategy] -->|Business Case| S1[S01: Specify]
    
    subgraph Definition
        S1 -->|PRD Draft| Ambiguity{Ambiguous?}
        Ambiguity -- Yes --> Clarify[Clarify Questions]
        Clarify --> S1
        Ambiguity -- No --> S2[S02: Plan]
    end

    subgraph Design
        S2 -->|PLAN.md + C4| Review1{Approved?}
        Review1 -- No --> S2
        Review1 -- Yes --> S3[S04: Tasks]
    end

    subgraph Execution
        S3 -->|project/TASKS.md| Decomp{Task < 15m?}
        Decomp -- No --> S3
        Decomp -- Yes --> S4[S05: Implement]
        
        S4 --> TDD[TDD Cycle]
        TDD --> RARV[RARV: Reason/Act/Reflect/Verify]
        RARV --> Audit[Audit Log]
    end

    Audit --> Done([Feature Complete])

Text SDLC (ASCII)

0. STRATEGY
   -> Why are we building this? (Value/ROI)
   v
1. SPECIFY (S01)
   -> spec-writer.skill -> PRD.md (EARS Syntax)
   -> (Optional) visual-specs.skill -> Visual Spec Package (UI/Arch from scans)
   -> Human Review & Approval
   v
2. PLAN (S02)
   -> arch-design.skill -> PLAN.md + C4 Diagrams
   -> adr-writer.skill -> Architectural Decisions
   v
3. DECOMPOSE (S03/S04)
   -> project/TASKS.md -> Atomic steps (<15 mins each)
   v
4. IMPLEMENT (S05)
   -> One task at a time
   -> TDD Loop: Red -> Green -> Refactor
   -> RARV Loop: Reason -> Act -> Reflect -> Verify
   v
5. VERIFY & SHIP (S06-S10)
   -> Integrity Check -> Security Audit -> Deploy

6. COGNITIVE ORCHESTRATION & HEALING
   -> gabbe brain activate -> Predict bottlenecks & route complexity
   -> gabbe brain evolve -> Meta-optimize failing skills into Semantic Memory
   -> gabbe brain heal -> Recover from environment/DB corruption

Agents Kit Structure Map

agents/
├── AGENTS.md                 # Universal config (edit per project)
├── CONSTITUTION.md           # Immutable project law
├── skills/                   # 214 .skill.md files
│   ├── 00-index.md           # Full skills registry
│   ├── coding/               # tdd, review, debug, refactor, git...
│   ├── architecture/         # arch-design, patterns, api-design...
│   ├── security/             # audit, threat-model, privacy...
│   ├── ops/                  # sre, docker, k8s, deploy, cost...
│   ├── product/              # spec-writer, req-elicitation...
│   ├── core/                 # research, self-heal, lifecycle...
│   ├── data/                 # data-engineering, db-migration
│   ├── coordination/         # multi-agent-orch, agent-protocol
│   └── brain/                # active-inference, consciousness, memory...
├── templates/                # 100 fill-in-the-blank documents
│   ├── 00-index.md           # Full templates registry
│   ├── coding/               # test plans, checklists, devcontainer
│   ├── architecture/         # ADR, C4, domain model, integration
│   ├── security/             # threat model, safety case, ethics
│   ├── ops/                  # incident, deploy, capacity, benchmark
│   ├── product/              # PRD, spec, user story, business case
│   ├── core/                 # plan, tasks, audit log, traceability
│   ├── coordination/         # agent profiles, swarm config
│   └── brain/                # inference loop, episodic memory, OODA
├── guides/                   # 86 language & domain guides
├── personas/                 # 36 specialized agent roles
├── memory/                   # Episodic + semantic + project state
└── docs/                     # Whitepapers & research

🧠 Brain Mode (Meta-Cognitive Layer)

[!NOTE] Experimental. Brain Mode, Loki Mode, Active Inference, Evolutionary Prompt Optimization, the self-healing loop, and forecasting are experimental research runtimes. They are gated behind explicit gabbe brain … subcommands (never on by default), covered by the test suite, and deterministically replayable (gabbe replay). See ADR-0002 for the design rationale and docs/VERIFICATION_GUIDE.md for how to reproduce each one. Treat outputs as advisory, not production guarantees.

Sits above Loki. Decides how to execute, not just what to execute.

| Feature | Description | |---|---| | Active Inference | Predict → Act → Observe → Compare → Adapt loop | | Cost Routing | Simple tasks → local free models, complex → remote SOTA | | Episodic Memory | Recalls past project outcomes to avoid repeated mistakes | | System 2 Thinking | Strategic planning before execution |

🔧 Setup by Project Type

JavaScript / TypeScript / Node.js

Guide: guides/js-ts-nodejs.md
Stack: Vitest, Zod, Prisma, Playwright, Hono
Config in AGENTS.md: test_cmd="npx vitest", lint_cmd="npx eslint ."

Go (Golang)

Guide: guides/go-lang.md
Stack: Echo/Gin, Ent, Testify, Testcontainers
Config in AGENTS.md: test_cmd="go test ./...", lint_cmd="golangci-lint run"

PHP / Laravel

Guide: guides/php-laravel.md
Stack: DDD, Actions, Pest PHP, PHPStan L9, Enlightn
Config in AGENTS.md: test_cmd="vendor/bin/pest", lint_cmd="vendor/bin/pint"

Python / FastAPI

Guide: guides/python-fastapi-ai.md
Stack: Pydantic, Pytest, Ruff, FastAPI
Config in AGENTS.md: test_cmd="pytest", lint_cmd="ruff check ."

📋 SDLC Phases (10 Gates)

The table below enumerates the 10 SDLC gates (S01–S10). These are bracketed by the Day-0 phase S00 (Strategy & Discovery) and the Day-2 phases S11–S13 (Operate / Evolve / Decommission) that complete the cradle-to-grave ADLC (S00–S13).

| Phase | Gate | Key Artifact | |---|---|---| | S01 | Requirements | PRD_TEMPLATE.md (EARS syntax) | | S02 | Design | ADR_TEMPLATE.md + C4 diagrams | | S03 | Specification | SPEC_TEMPLATE.md + API contracts | | S04 | Tasks | TASKS_TEMPLATE.md (15-min rule) | | S05 | Implementation | TDD Red→Green→Refactor + RARV | | S06 | Testing | Unit >99% + integration + E2E | | S07 | Security | SECURITY_CHECKLIST.md + audit | | S08 | Review | Human code review | | S09 | Staging | Smoke tests + benchmarks | | S10 | Production | Rollback plan + monitoring |

🛠️ Skills Summary (by Category)

| Category | Count | Key Skills | |---|---|---| | Coding | 10+ | tdd-cycle, debug, refactor, code-review, git-workflow | | Architecture | 15+ | arch-design, microservices, systems-architecture, system-scalability, blockchain-dlt | | Operations | 15+ | reliability-sre, production-health, dev-environments, cost-optimization | | Security | 15+ | security-audit, secure-architecture, privacy-data-protection, api-security | | Product | 10+ | spec-writer, req-elicitation, visual-specs, green-software | | Core | 10+ | research, self-heal, knowledge-gap, meta-optimize | | Data | 5+ | data-engineering, db-migration, semantic-web | | Coordination | 5+ | multi-agent-orch, agent-protocol | | Brain | 10+ | active-inference, consciousness-loop, cost-benefit-router | | AI/Swarm | 5+ | multi-agent-systems, agent-communication, beyond-llms | | Industry | 5+ | healthcare-fhir, telecom-networks, industrial-iot, global-standards, engineering-standards | | Loki Modes | 2+ | brain-mode, loki-mode |

Full catalog: agents/skills/00-index.md (generated by init.py)

📝 Templates Summary (by Category)

| Category | Count | Examples | |---|---|---| | Coding | 5+ | Clean Code Checklist, Test Plan, E2E Suite | | Architecture | 10+ | ADR, C4, Domain Model, Scalability Plan, Smart Contract | | Ops | 5+ | Incident Postmortem, Deploy Config, Capacity Plan, FinOps | | Security | 5+ | Threat Model, Safety Case, Ethical Impact | | Product | 10+ | PRD, Spec, User Story Map, Visual Spec, Green Software | | Core | 5+ | Plan, Tasks, Audit Log, Project State, Continuity | | Coordination | 5+ | Agent Profile, Swarm Config, Handshake, Voting Log | | Brain | 5+ | Active Inference Loop, Episodic Memory, OODA Trace | | Data | 5+ | Data Pipeline, Database Schema, OWL Ontology | | Industry | 5+ | FHIR Interop, IoT Telemetry, Telecom API, Global SDLC Audit |

Full catalog: agents/templates/00-index.md (generated by init.py)

🔌 Essential MCP Servers

| Server | Purpose | |---|---| | Context-7 | Up-to-date SDK docs (prevents hallucination) | | Sequential Thinking | Chain-of-thought reasoning | | GitHub MCP | PR review, code search | | PostgreSQL MCP | Live schema introspection | | Playwright MCP | Browser automation / visual TDD | | Brave Search | Authoritative web research | | Time Complexity | Local Big-O static analysis via tree-sitter | | Excalidraw | Programmatic Excalidraw diagram creation |

Config: templates/core/MCP_CONFIG_TEMPLATE.json · Full guide: MCP_CONFIGURATIONS.md

🔄 Self-Healing Loop

Task → Knowledge gap? → research.skill → Execute → Verify
                                          ↓ FAIL
                                   self-heal.skill (max 5×)
                                          ↓ STILL FAIL
                                   Human escalation report

📊 Memory Architecture

| Layer | Location | Purpose | |---|---|---| | Project State | agents/memory/PROJECT_STATE.md | Current SDLC phase | | Audit Log | agents/memory/AUDIT_LOG.md | Append-only decision history | | Continuity | agents/memory/CONTINUITY.md | Past failures (read every session) | | Episodic | agents/memory/episodic/ | Per-session decision logs | | Semantic | agents/memory/semantic/ | Crystallized project knowledge |

🚨 Troubleshooting

| Problem | Fix | |---|---| | Agent ignores AGENTS.md | Run setup-context.sh to create symlinks | | Agent repeats mistakes | Check CONTINUITY.md — tell agent to read it | | Tests pass immediately | False positive — test is broken, fix it first | | Uses deprecated APIs | Activate Context-7 MCP | | Session lost | "Use session-resume skill to load all memory" | | Context too large | Use context_cost: low skills or activate Loki Mode |

📚 Guides by Stack

| Stack | Guide | |---|---| | JS/TS/Node.js | guides/languages/js-ts-nodejs.md | | Node.js Advanced & TS | guides/languages/nodejs-advanced.md | | Go | guides/languages/go-lang.md | | PHP/Laravel | guides/languages/php-laravel.md | | Python/FastAPI | guides/languages/python-fastapi-ai.md | | SQL/NoSQL | guides/data/sql-nosql.md | | Microservices | guides/architecture/microservices.md | | Architecture | guides/architecture/systems-architecture.md | | Testing | guides/principles/testing-strategy.md | | Design Patterns | guides/patterns/design-patterns.md | | Agentic AI | guides/ai/ai-agentic.md | | Multi-Agent Systems | guides/ai/multi-agent-systems.md | | Security/Compliance | guides/ops/compliance-audit.md | | API Security | guides/security/api-security.md | | Cryptography | guides/security/cryptography-standards.md | | Data Protection | guides/security/privacy-data-protection.md | | Secure Arch | guides/security/secure-architecture.md | | Threat Modeling | guides/security/threat-modeling.md | | DevOps/Environments | guides/ops/dev-environments.md | | Developer Workflow | guides/ops/dev-workflow.md | | Legacy/COBOL | guides/principles/legacy-tech.md | | Future Tech 2030 | guides/principles/future-tech.md | | C-Sharp / .NET | guides/languages/c-sharp.md | | Self-Healing | guides/ai/self-healing-summary.md | | Time Complexity | guides/patterns/time-complexity-analysis.md | | Troubleshooting | guides/ops/troubleshooting-guide.md | | Enterprise Migration | guides/patterns/enterprise-migration-scenario.md |

🛡️ Security & Guardrails

All 214 skills in the GABBE kit feature a heavily enforced "Security & Guardrails" section that binds agents to the project's CONSTITUTION.md. The 3-layer security constraints include:

Skill Security: Tool-specific protection (e.g., preventing command injection or sandbox escapes).
System Integration Security: Safe external integration (e.g., verifying boundary enforcement or ensuring test coverage).
LLM/Agent Guardrails: Protection from AI-specific failures (e.g., hallucinated metrics, confirmation bias, or prompt injection).

Agents are explicitly configured to Fail-Closed—they must wait for human approval rather than bypassing a guardrail.

🚀 GABBE CLI (Experimental)

GABBE has also an experimental helper, Zero-Dependency CLI (gabbe) for a "Hybrid Mode" (Markdown files and a SQLite database) and launching different commands. It is a work-in-progress and you can do without it, only with the rest of the kit.

Prerequisites

Python 3.8+
LLM API Key: For Brain/Route features, set GABBE_API_KEY (OpenAI-compatible).

Environment Variables (full reference in CLI_REFERENCE.md):

| Variable | Default | Description | |---|---|---| | GABBE_API_URL | https://api.openai.com/v1/chat/completions | OpenAI-compatible endpoint | | GABBE_API_KEY | (required for LLM features) | Bearer token for the LLM API | | GABBE_API_MODEL | gpt-4o | Model name sent in API requests | | GABBE_LLM_TEMPERATURE | 0.7 | Sampling temperature (0.0–1.0) | | GABBE_LLM_TIMEOUT | 30 | HTTP timeout in seconds | | GABBE_LLM_MAX_RETRIES | 3 | Number of LLM retry attempts on transient errors | | GABBE_LLM_CACHE | false | Opt-in: cache identical LLM calls locally (0 tokens on a hit; only for deterministic calls) | | GABBE_LOG_LEVEL | INFO | Logging verbosity (DEBUG, INFO, WARNING, ERROR) | | GABBE_ROUTE_THRESHOLD | 50 | Complexity score above which prompts route REMOTE | | GABBE_MAX_COST_USD | 5.0 | Maximum cost (USD) budget per run | | GABBE_MAX_TOKENS_PER_RUN | 100000 | Maximum token limit per run | | GABBE_MAX_TOOL_CALLS_PER_RUN | 50 | Maximum tool calls allowed per run | | GABBE_MAX_ITERATIONS | 25 | Maximum active-inference iterations per run | | GABBE_MAX_WALL_TIME | 300 | Maximum wall-clock time (seconds) per run | | GABBE_MAX_RECURSION_DEPTH | 5 | Maximum agent recursion depth | | GABBE_MAX_RETRIES_PER_TOOL | 3 | Maximum retries for a single tool call | | GABBE_POLICY_FILE | project/policies.yml | Path to the YAML policy file | | GABBE_ESCALATION_MODE | cli | Escalation mode: cli, file, or silent | | GABBE_SUBPROCESS_TIMEOUT | 300 | Timeout (seconds) for verify sub-commands | | GABBE_OTEL_ENABLED | false | Enable OpenTelemetry tracing |

Installation

The CLI is a Python package, published on PyPI as gabbe.

# 1a. Install from PyPI (recommended)
pipx install gabbe          # or: pip install gabbe  /  uvx gabbe

# 1b. …or install locally from a checkout (for development)
pip install -e .

# 2. Verify installation
gabbe --help

Core Commands

| Command | Description | |---|---| | gabbe init | Initialize the SQLite Database (Run this after python scripts/init.py). | | gabbe sync | Hybrid Sync: Bidirectional sync between project/TASKS.md and SQLite DB. | | gabbe verify| Enforcer: programmable integrity check (files, tests, lint). | | gabbe status| Dashboard: Visualizes project phase and task progress. | | gabbe brain | Meta-Cognition: Activates Active Inference loop or Evolutionary Prompt Optimization (Requires API Key). | | gabbe route | Cost Router: Arbitrates between Local and Remote LLMs based on task complexity (Requires API Key). | | gabbe forecast| Strategic Forecast: Projects remaining work cost and tokens based on historical run data. | | gabbe serve-mcp | MCP Gateway: Zero-dependency JSON-RPC Model Context Protocol server for standalone agents to access tools safely. | | gabbe runs | Run History: List recent agent runs with status, cost, and timestamps. | | gabbe audit <run-id> | Audit Trace: Display structured span-level trace for a past run (--format json\|table). | | gabbe replay <run-id> | Deterministic Replay: Replay a past run from its checkpoints (--from-step N). | | gabbe resume <run-id> | Escalation Resume: Approve or reject pending escalations for a paused run. | | gabbe registry publish | Publish Skills: Export the kit's skills as a publish-ready agentskills.io bundle (manifest + agent-card) for universal registries. | | gabbe registry add <source> | Import Skills: Draw an external skill/bundle (path, .tar.gz, or URL) — validated + security-scanned + landed namespaced for review. | | gabbe setup | Install Wizard: Run the interactive installer to wire the kit into your coding agents (see also npx gabbe-kit init). |

Platform Control Layer

The experimental gabbe CLI supports a platform control layer. It covers budget enforcement, cost and token controls, hard stops, policy rules, the tool gateway, audit tracing, human escalation, and deterministic replay. Detailed documentation is available in PLATFORM_CONTROLS.md.

Architecture

GABBE uses a Hybrid Architecture where agents and humans interact via Markdown, but the system of record is SQLite.

graph TD
    subgraph User["User (Legacy Flow)"]
        Edit[Edit project/TASKS.md]
    end

    subgraph CLI["GABBE CLI (pip installed)"]
        Sync[gabbe sync]
        Verify[gabbe verify]
        Brain[gabbe brain]
        Router[gabbe route]
        Forecast[gabbe forecast]
        MCP[gabbe serve-mcp]
    end

    subgraph Storage["Hybrid Memory"]
        MD[Markdown Files]
        DB[(SQLite state.db)]
    end

    User -->|Manual Edits| MD
    MD <-->|Bi-Directional| Sync
    Sync <--> DB
    Brain -->|Read/Write| DB
    Verify -->|Check| MD
    Verify -->|Check| DB
    Forecast -->|Analyze| DB
    MCP -->|Write Telemetry| DB

How to Use

Setup

# 1. Generate Context Configs
python3 scripts/init.py

# 2. Initialize Database
gabbe init

Daily Workflow

# Check status
gabbe status

# Sync tasks (manual edits)
gabbe sync

# Optimize a skill (Requires GABBE_API_KEY)
gabbe brain evolve --skill tdd-cycle

Verification

gabbe verify

License

GABBE is dual-licensed so the executable engine and the knowledge content can each use the license appropriate to it:

| Part | License | Files | | --- | --- | --- | | Code | Apache-2.0 | gabbe/, scripts/, agents/scripts/ (all .py) | | Content | CC BY-SA 4.0 | agents/ Markdown (skills, templates, guides, personas), docs/ |

SPDX expression: Apache-2.0 AND CC-BY-SA-4.0. Apache-2.0 adds an explicit patent grant and is OSI-approved, making the CLI safe to embed in downstream (including proprietary) projects; the curated Markdown corpus stays copyleft under CC BY-SA 4.0. Existing users lose no rights — this is purely additive to the prior CC-BY-SA-4.0-only declaration.